In moodymudskipper/cutr: Enhanced cut And Useful Related Functions

knitr::opts_chunk$set(
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cutr extends base::cut.default's possibilities, getting inspiration from existing alternatives such as Hmisc::cut2 and the ggplot2::cut_* family of functions, but going much further.

default behavior + closed and open_end

We build a distribution and show the default behavior, in the rest of the document we'll show results using table as it's more telling.

library(cutr)
x <- c(rep(1,3),rep(2,2),3:6,17:20)
hist(x,breaks = 0:20)
cuts <- c(0,5,10,20)
smart_cut(x,cuts)
table(smart_cut(x,cuts))

The output is very similar to what we get with base or Hmisc and ggplot2 alternatives.

table(base::cut(x,cuts))
table(Hmisc::cut2(x,cuts))
table(smart_cut(x,cuts,"breaks"))

A the difference with base is that we close by default the left size, this can be changed by setting the closed parameter to "right".

Another is that the ends are both closed in smart_cut, this can be changed by setting the open_end parameter to FALSE

closed is borrowed from ggplot2::cut_* functions, and corresponds to the RIGHT parameter of base::cut.default.

open_end corresponds to the (negated) include.lowest parameter of base::cut.

The difference with Hmisc is due to the formatting function, which is formatC for cut and smart_cut, and format for Hmisc (which gives all labels the same width). The display of smart_cut is highly flexible thanks to the argument format_fun detailed further in this document.

what and i

The what parameter determines how cuts will be chosen, depending on the value of i.

table(smart_cut(x,cuts,"breaks"))                   # fixed breaks
table(smart_cut(x,2,"groups"))                      # groups defined by quantiles
table(smart_cut(x,list(2,"balanced"),"groups"))     # optimized groups of equal size
table(smart_cut(x,3,"n_by_group"))                  # try to get 3 items by group using quantiles
table(smart_cut(x,list(3,"balanced"),"n_by_group")) # try to get 3 items by group using optimization
table(smart_cut(x,3,"n_intervals"))                 # intervals of equal width
table(smart_cut(x,7,"width"))                       # interval of equal defined width, start on 1st value
table(smart_cut(x,list(7,"right"),"width"))         # interval of equal defined width, end on last value
table(smart_cut(x,list(6,"centered"),"width"))      # interval of equal defined width, centered
table(smart_cut(x,list(6,"centered0"),"width"))     # interval of equal defined width, centered on 0
table(smart_cut(x,list(7,0),"width"))               # interval of equal defined width, starting on 0
table(smart_cut(x,3,"cluster"))                     # create groups by running a kmeans clustering

simplify

TRUE by default, when a value is the only one in its group, display it as a label, without brackets. Similar to oneval in Hmisc::cut2.

table(smart_cut(x, 5, "width"))
table(smart_cut(x, 5, "width", simplify = FALSE))

expand

expand makes sure all values from x will be in an interval by expanding the cut points. base::cut.default never expands, Hmisc::cut2 always expands.

table(smart_cut(x,c(4,10,18)))
table(smart_cut(x,c(4,10,18),expand = FALSE))

crop

crop is FALSE by default, if TRUE the side intervals are reduced to fit the data.

table(smart_cut(x,c(0,10,30)))
table(smart_cut(x,c(0,10,30),crop = TRUE))

squeeze

squeeze is FALSE by default, if TRUE every interval is reduced to fit the data.

table(smart_cut(x,c(0,10,30)))
table(smart_cut(x,c(0,10,30),squeeze = TRUE))

brackets + sep

Different brackets can be chosen

table(smart_cut(x,c(0,10,30), brackets = c("]","[","[","]")))
table(smart_cut(x,c(0,10,30), brackets = NULL, sep = "~", squeeze= TRUE))

labels

labels can be a vector just like in base::cut.default, but it can also be a function of 2 arguments, which are a vector of values contained in the interval and a vector of cutpoints.

table(smart_cut(x,c(4,10)))
table(smart_cut(x,c(4,10),labels = ~mean(.x)))   # mean of values by interval
table(smart_cut(x,c(4,10),labels = ~mean(.y)))   # center of interval
table(smart_cut(x,c(4,10),labels = ~median(.x))) # median
table(smart_cut(x,c(4,10),labels = ~paste(
  sep="~",.y[1],round(mean(.x),2),.y[2]))) # a more sophisticated label

format_fun

With cutr the user can provide any formating function through the argument format_fun, including the package function format_metric.

table(smart_cut(x^6 + x/100,5,"g"))
table(smart_cut(x^6 + x/100,5,"g",format_fun = format, digits = 3))
table(smart_cut(x^6,5,"g",format_fun = signif))
table(smart_cut(x^6,5,"g",format_fun = smart_signif))
table(smart_cut(x^6,5,"g",format_fun = format_metric))

more on groups

groups and n_by_group try to place cut points at relevant quantile positions, we won't get the required number of groups if several quantiles fall on the same value, to remedy to this we can use an optimization function.

The most straightforward way to optimize bin size, and the only way most will ever need, is to minimize the variance between the target bin size and the actual binsize, this is what happens when the i argument is list(n, "balanced").

table(smart_cut(x,3,"groups"))
table(smart_cut(x,list(3,"balanced"),"groups"))

the second element of the list can be a string (which will be mapped to a predefined function) or a custom made 2 argument function that is applied on all possible bin combinations, its arguments are : bin size the cut points

The combination of cut points that returns the lowest value when passed to this function will be selected (or the first of them if the minimum is not unique).

cutf and cutf2

These are copies of base::cut.default and Hmisc::cut2 with the difference that the formatting function can be used freely. All the features are contained in smart_cut but these functions allow users to keep the interface, and defaults of the function they know and to modify existing code easily, for example to leverage format_metric with minimal effort.

moodymudskipper/cutr documentation built on Aug. 23, 2019, 7:15 p.m.